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    {
      "cell_type": "code",
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      "source": [
        "%matplotlib inline"
      ]
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      "source": [
        "\n# The Iris Dataset\n\nThis data sets consists of 3 different types of irises'\n(Setosa, Versicolour, and Virginica) petal and sepal\nlength, stored in a 150x4 numpy.ndarray\n\nThe rows being the samples and the columns being:\nSepal Length, Sepal Width, Petal Length and Petal Width.\n\nThe below plot uses the first two features.\nSee `here <https://en.wikipedia.org/wiki/Iris_flower_data_set>`_ for more\ninformation on this dataset.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      "source": [
        "print(__doc__)\n\n\n# Code source: Ga\u00ebl Varoquaux\n# Modified for documentation by Jaques Grobler\n# License: BSD 3 clause\n\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom sklearn import datasets\nfrom sklearn.decomposition import PCA\n\n# import some data to play with\niris = datasets.load_iris()\nX = iris.data[:, :2]  # we only take the first two features.\ny = iris.target\n\nx_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\ny_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n\nplt.figure(2, figsize=(8, 6))\nplt.clf()\n\n# Plot the training points\nplt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Set1,\n            edgecolor='k')\nplt.xlabel('Sepal length')\nplt.ylabel('Sepal width')\n\nplt.xlim(x_min, x_max)\nplt.ylim(y_min, y_max)\nplt.xticks(())\nplt.yticks(())\n\n# To getter a better understanding of interaction of the dimensions\n# plot the first three PCA dimensions\nfig = plt.figure(1, figsize=(8, 6))\nax = Axes3D(fig, elev=-150, azim=110)\nX_reduced = PCA(n_components=3).fit_transform(iris.data)\nax.scatter(X_reduced[:, 0], X_reduced[:, 1], X_reduced[:, 2], c=y,\n           cmap=plt.cm.Set1, edgecolor='k', s=40)\nax.set_title(\"First three PCA directions\")\nax.set_xlabel(\"1st eigenvector\")\nax.w_xaxis.set_ticklabels([])\nax.set_ylabel(\"2nd eigenvector\")\nax.w_yaxis.set_ticklabels([])\nax.set_zlabel(\"3rd eigenvector\")\nax.w_zaxis.set_ticklabels([])\n\nplt.show()"
      ]
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